A New Approach to Soil Initialization for Studying Subseasonal Land-Atmosphere Interactions

Numerical experiments on sensitivity to land surface initializations are frequently conducted to investigate the predictability and uncertainties of hydrometeorological extremes. However, the conventional approaches to soil initialization often assume synchronized extremes over the target region, creating initial conditions that violate the intrinsic spatial pattern of hydrometeorological variability. Here we propose a "Slope" approach to accommodate unsynchronized anomalies, which creates initial conditions of a variable (soil temperature or soil moisture) across a large domain based on the slopes of linear regression between the variable averaged over a small target region and at each grid point in the surrounding regions. Within the target region, the "Slope" approach produces spatial patterns and temporal evolutions of hydrometeorological responses similar to the conventional approach, but generates stronger signals probably due to the nonlocal impact (excluded from the conventional approach). In the surrounding regions, the hydrometeorological responses in the "Slope" approach are consistent with the spatiotemporal variability of the model climate. Slope-based experiments targeting different adjacent regions produce similar results, suggesting that one ensemble of experiments targeting one region may be sufficient to represent the responses from multiple ensembles each targeting a different region and thus providing the basis for increasing the computational efficiency of some land-atmosphere interaction studies. While South America is used to demonstrate the concept in this study, the new approach offers the most advantages in regions with spatially unsynchronized or even anti-phased hydrometeorological extremes.